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Detecting Sources of Transcriptional Heterogeneity in Large-Scale RNA-Seq Data Sets.

ABSTRACT: Gene expression levels are dynamic molecular phenotypes that respond to biological, environmental, and technical perturbations. Here we use a novel replicate-classifier approach for discovering transcriptional signatures and apply it to the Genotype-Tissue Expression data set. We identified many factors contributing to expression heterogeneity, such as collection center and ischemia time, and our approach of scoring replicate classifiers allows us to statistically stratify these factors by effect strength. Strikingly, from transcriptional expression in blood alone we detect markers that help predict heart disease and stroke in some patients. Our results illustrate the challenges and opportunities of interpreting patterns of transcriptional variation in large-scale data sets.

SUBMITTER: Searle BC

PROVIDER: S-EPMC5161273 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Detecting Sources of Transcriptional Heterogeneity in Large-Scale RNA-Seq Data Sets.

Searle Brian C BC Gittelman Rachel M RM Manor Ohad O Akey Joshua M JM

Genetics 20161011 4

Gene expression levels are dynamic molecular phenotypes that respond to biological, environmental, and technical perturbations. Here we use a novel replicate-classifier approach for discovering transcriptional signatures and apply it to the Genotype-Tissue Expression data set. We identified many factors contributing to expression heterogeneity, such as collection center and ischemia time, and our approach of scoring replicate classifiers allows us to statistically stratify these factors by effec ...[more]

PMID: 27729424

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